Precise timing of ovulation is required for reproductive success. Ovulation is triggered when estradiol switches from negative feedback action on the pituitary and hypothalamus to positive feedback, initiating a surge of gonadotropin-releasing hormone (GnRH) secretion that causes a surge of luteinizing hormone (LH) release, which triggers ovulation. Our understanding of the neurobiological changes underlying the switch from negative to positive feedback is incomplete. High levels of estradiol are essential and, in both humans and rodents, the LH surge tends to occur at a specific time of day. GnRH neurons, however, do not express the estrogen receptor required for feedback, thus estradiol-sensitive afferents likely convey estradiol information to GnRH neurons. Likewise, time-of-day information is relayed directly or indirectly from the central mammalian clock in the suprachiasmatic nucleus (SCN). Our working hypothesis is that GnRH neurons switch from negative to positive feedback by integrating multiple changes to their synaptic inputs and intrinsic properties. To test this hypothesis, we will use three separate approaches in three aims. In Aim1, we will test the hypothesis that SCN neuromodulator vasoactive intestinal peptide acts directly on GnRH neurons to change intrinsic properties resulting in increased action potential firing rate. In Aim 2, we hypothesize that changes to transcription and translation occur during the transition from negative to positive feedback. To test this, we will use isolate mRNA transcripts bound to GFP-tagged ribosomal subunits that are targeted to GnRH neurons using cre recombinase under the control of the GnRH promoter. This will allow us to profile the entire transcriptome during this feedback transition. The differences we observe may have a role in modifying intrinsic properties that mediate the switch. In Aim 3, we will develop a mathematical model of a GnRH neuron that incorporates all the individual changes to intrinsic properties and synaptic transmission observed in the literature and in Aims 1 and 2. While electrophysiological experiments are limited to manipulating one or two variables at a time, mathematical models have the advantage of being able to integrate and simultaneously manipulate all variables. By iteratively adjusting conductances, we will be able to predict which combination or combinations of properties reproduce the switch from negative to positive feedback. This model will generate testable hypotheses. Our long-term goal is to advance the fundamental understanding of the signals required for neural control of ovulation, and how perturbations to this system lead to infertility. The mathematical models we develop here to integrate estradiol and SCN signals will help us to devise protocols to understand the integration of other signals, such as metabolism, into reproductive function. Furthermore, this research will train me in RNA isolation and profiling electrophysiological techniques and hypothesis generation using mathematical modeling. This training is essential for my evolution into an independent wet and dry lab physician scientist.